Semantic segmentation is a classical problem in computer vision, which is important in the field of autonomous driving. Although significant progress has been achieved in semantic segmentation, its generalization ability to unknown domains is still challenging. To effectively solve this problem, a semantic segmentation method ImDeeplabV3plus with instance selective whitening loss is proposed in this paper. DeeplabV3plus is selected as the baseline. In order to enhance the representation of the region of interest, the coordinate attention (CA) mechanism is added. To better integrate multiple low‐level features, the adaptively spatial feature fusion (ASFF) is employed to adaptively learn the importance of features at different levels for each location. For preferably coping with the domain changes, an instance selective whitening (ISW) loss is introduced in the early stage of the backbone. The model is trained with the Cityscapes dataset and then applied to the unknown domain RobotCar dataset. Compared with DeeplabV3plus, the authors’ ImDeeplabV3plus model shows 1.29% mIoU improvement. When ISW loss is added, 2.08% improvement in mIoU is achieved compared with ImDeeplabV3plus. Experimental results show that the proposed method is simple and improves the domain generalization ability.


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    Title :

    ImDeeplabV3plus with instance selective whitening loss in domain generalization semantic segmentation


    Contributors:
    Zhang, You (author) / Chen, Houjin (author) / Li, Yanfeng (author) / Zhou, Junqi (author)

    Published in:

    Publication date :

    2023-01-01


    Size :

    13 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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